Depression and schizophrenia recognition method

Abstract

The invention relates to a depression and schizophrenia recognition method. The depression and schizophrenia recognition method comprises the following steps of S1, obtaining the electroencephalogramdata of a patient during opening eyes and closing eyes under resting state; S2, pretreating the electroencephalogram data in the step S1; S3, converting the data obtained during pretreatment in the step S2 into power spectral density PSD; S4, extracting various characteristics of PSD in the step S3, and constructing a characteristic vector; S5, with the characteristic vectors in the step S4 as input of a classifier, classifying the data; S6, performing model assessment on the classification results in the step S5, so as to obtain a classification model being good in generalization ability; andS7, inputting a test set into the classifier model in the step S6, performing classification, and realizing depression and schizophrenia recognition. According to the depression and schizophrenia classification method, a support vector machine classification method is used for treating and classifying the electroencephalogram of the patient, and the method has good divisibility for data, and is higher in accuracy than other classification methods.

Description

technical field [0001] The invention relates to the technical field of resting-state EEG, in particular to a method for identifying depression and schizophrenia. Background technique [0002] Schizophrenia (SCZ) and depression (DP) are high-risk and high-risk diseases in modern society. Timely and accurate diagnosis will be very beneficial to the prevention and treatment of such diseases. Electroencephalogram (EEG) is the electrical activity signal of nerve cells recorded through the cerebral cortex. Studies have shown that the EEG of patients with depression and schizophrenia is in rhythm. , amplitude, power and other parameters show abnormal phenomena, so the comparative analysis and research of EEG is helpful for the differential diagnosis of these two diseases, but the existing classification methods currently on the market have low accuracy. Contents of the invention [0003] In order to solve the above problems, the object of the present invention is to provide a me...

AI Technical Summary

Problems solved by technology

Timely and accurate diagnosis will be very beneficial to the prevention and treatment of such diseases. Electroencephalogram (EEG) is the electrical activity signal of nerve cells recorded through the cerebral cortex. Studies have shown that the EEG of patients with depression and schizophrenia is in rhythm. , amplitude, power and other parameters show abnormal phenomena, so the comparative analysis and research of EEG is helpful for the differential diagnosis of these two diseases, but the existing classification methods currently on the market have low accuracy

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